We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and European Union disaggregate data. Our approach allows us to accurately estimate both the number and timing of breaks in the Phillips curve. It further allows us to determine the...

Many personal and policy decisions turn on perceptions of school effectiveness, defined here as the causal effect of attendance at a particular school or set of schools on student test scores and other outcomes. Widely-disseminated school ratings frameworks compare average student achievement...

Sparse models, though long preferred and pursued by social scientists, can be ineffective or unstable relative to large models, for example, in economic predictions (Giannone et al., 2021). To achieve sparsity for economic interpretation while exploiting big data for superior empirical...

We develop an empirical Bayes ranking procedure that assigns ordinal grades to noisy measurements, balancing the information content of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the race and gender...

We propose a novel Bayesian Graded Response Model (BGRM) for food security measurement. Our BGRM has several attractive features. It produces continuous food security estimates and measures of estimation uncertainty at the household level. Unlike the USDA's official measurement model, the BGRM...

After decades of rising global economic integration, the world economy is now fragmenting. To measure this phenomenon, we introduce an index of geopolitical fragmentation derived from various empirical indicators. This index is developed using a flexible dynamic factor model with time-varying...

This paper estimates consumer demand for firearms with the aim of predicting the likely impacts of firearm regulations on the number and types of guns in circulation. We first conduct a stated-choice-based conjoint analysis and estimate an individual-level demand model for firearms. We validate...

We propose a methodology to take dynamic stochastic general equilibrium (DSGE) models to the data based on the combination of differentiable state-space models and the Hamiltonian Monte Carlo (HMC) sampler. First, we introduce a method for implicit automatic differentiation of perturbation...

In this paper we use the functional vector autoregression (VAR) framework of Chang, Chen, and Schorfheide (2024) to study the effects of monetary policy shocks (conventional and informational) on the cross-sectional distribution of U.S. earnings (from the Current Population Survey), consumption,...